Relevant bibliographies by topics / Flood and Flash Flood forecasting

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  2. Dissertations / Theses
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Academic literature on the topic 'Flood and Flash Flood forecasting'

Author: Grafiati
Published: 9 March 2023

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Journal articles on the topic "Flood and Flash Flood forecasting"

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Chen, Y., J. Li, S. Huang, and Y. Dong. "Study of Beijiang catchment flash-flood forecasting model." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 150–55. http://dx.doi.org/10.5194/piahs-368-150-2015.

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Abstract. Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences Discussions 5, no. 1 (February 12, 2008): 345–77. http://dx.doi.org/10.5194/hessd-5-345-2008.

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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground "truth". The lack of observations in most flash flood prone basins, therefore, lead to develop a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area with leadtimes of the order of the weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with a threshold – exceedance. As case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. The short-range weather forecasts, from the Lokalmodell of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences 12, no. 4 (July 30, 2008): 1039–51. http://dx.doi.org/10.5194/hess-12-1039-2008.

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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 h in advance.
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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, and Jinggang Chu. "Flash Flood Forecasting Based on Long Short-Term Memory Networks." Water 12, no. 1 (December 29, 2019): 109. http://dx.doi.org/10.3390/w12010109.

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Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1–5 h lead time and range from 84.2% to 89.5% at 6–10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.
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Chang, Tzu-Yin, Hongey Chen, Huei-Shuin Fu, Wei-Bo Chen, Yi-Chiang Yu, Wen-Ray Su, and Lee-Yaw Lin. "An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods in the Southwestern Plain Areas of Taiwan." Water 13, no. 4 (February 4, 2021): 405. http://dx.doi.org/10.3390/w13040405.

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A pluvial flash flood is rapid flooding induced by intense rainfall associated with a severe weather system, such as thunderstorms or typhoons. Additionally, topography, ground cover, and soil conditions also account for the occurrence of pluvial flash floods. Pluvial flash floods are among the most devastating natural disasters that occur in Taiwan, and these floods always /occur within a few minutes or hours of excessive rainfall. Pluvial flash floods usually threaten large plain areas with high population densities; therefore, there is a great need to implement an operational high-performance forecasting system for pluvial flash flood mitigation and evacuation decisions. This study developed a high-performance two-dimensional hydrodynamic model based on the finite-element method and unstructured grids. The operational high-performance forecasting system is composed of the Weather Research and Forecasting (WRF) model, the Storm Water Management Model (SWMM), a two-dimensional hydrodynamic model, and a map-oriented visualization tool. The forecasting system employs digital elevation data with a 1-m resolution to simulate city-scale pluvial flash floods. The extent of flooding during historical inundation events derived from the forecasting system agrees well with the surveyed data for plain areas in southwestern Taiwan. The entire process of the operational high-performance forecasting system prediction of pluvial flash floods in the subsequent 24 h is accomplished within 8–10 min, and forecasts are updated every six hours.
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Saharia, Manabendra, Pierre-Emmanuel Kirstetter, Humberto Vergara, Jonathan J. Gourley, Yang Hong, and Marine Giroud. "Mapping Flash Flood Severity in the United States." Journal of Hydrometeorology 18, no. 2 (January 25, 2017): 397–411. http://dx.doi.org/10.1175/jhm-d-16-0082.1.

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Abstract Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.
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Ren, Juanhui, Bo Ren, Qiuwen Zhang, and Xiuqing Zheng. "A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region." Water 11, no. 9 (September 5, 2019): 1848. http://dx.doi.org/10.3390/w11091848.

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Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.
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Nguyen Van Ha, Tran Dang Hung, Doan Tran Anh, Giang Hoang Hiep, Nguyen Thi Huyen Trang, and Doan Ha Phong. "APPLICATION OF GIS AND REMOTE SENSING FOR MAPPING FLASH FLOOD RISE IN HOA BINH PROVINCE UNDER CLIMATE CHANGE CONTEXT." Tạp chí Khoa học Biến đổi khí hậu, no. 23 (December 28, 2022): 53–68. http://dx.doi.org/10.55659/2525-2496/23.75013.

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Hoa Binh is one of the provinces strongly suffering from natural disasters, especially flash floods. High slope mountainous terrains, reduced vegetation cover and unfavorable weather conditions form favorable conditions for flash floods to occur. This article develops a map of flash flood risk zoning in Hoa Binh using remote sensing and GIS technology. First, the factors affecting the risk of flash floods are identified, and each factor is classified based on the level of influence, then proceed to overlay the component maps causing flash floods. Factors affecting flash flood risk include: Slope, soil type, land use type, forest cover density and rain. As a result, areas at risk of flash floods are identified with 3 level: High, medium and low. This information can be used as a basis for forecasting areas at high risk of flash floods in the province.
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Nadeem, Muhammad Umer, Zeeshan Waheed, Abdul Mannan Ghaffar, Muhammad Mashood Javaid, Ameer Hamza, Zain Ayub, Muhammad Asim Nawaz, et al. "Application of HEC-HMS for flood forecasting in hazara catchment Pakistan, south Asia." International Journal of Hydrology 6, no. 1 (January 17, 2022): 7–12. http://dx.doi.org/10.15406/ijh.2022.06.00296.

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Floods have become more severe and frequent as a result of climate change around the world, posing a hazard to public safety and economic development. This study investigates the use of distributed hydrological models in flash flood risk management in a small watershed in Hazara, Pakistan, with the goal of improving Pakistan's early warning lead time. First, the HEC-HMS model was built using geographic data and the river network's structure, then calibrated and verified using eight high rainfall events from 2013. demonstrating that the HEC-HMS model could simulate floods in the research area Second, given that rainfall and flood events have happened, this paper proposes an analysis approach for a flood forecasting and warning system, as well as criteria for sending urban-stream flash flood alerts based on rainfall, in order to provide sufficient lead time. The DEMs (digital elevation models) of the research regions were processed using HEC-Geo HMS, an ArcView GIS tool for catchment delineation, terrain pre-processing, and basin processing. The model was calibrated and verified using previously observed data. The proposed flood prediction and risk reduction methodology is nonstructural. The Hydrologic Modeling System (HEC-HMS), which provides a sufficient lead time forecast and computes the runoff/stage threshold conditions, is at the heart of the flood warning application. For flood risk assessment, data from the Pakistan Meteorological Department (PMD) is entered into a hydro-meteorological database and then into the HEC-HMS. A server-client application was utilised to visualise the real-time flood scenario and send out an early warning message. The outcomes of this study will be used to develop flood validation measures in the Hazara stream watershed to deal with potential flash floods.
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Martinaitis, Steven M., Jonathan J. Gourley, Zachary L. Flamig, Elizabeth M. Argyle, Robert A. Clark, Ami Arthur, Brandon R. Smith, Jessica M. Erlingis, Sarah Perfater, and Benjamin Albright. "The HMT Multi-Radar Multi-Sensor Hydro Experiment." Bulletin of the American Meteorological Society 98, no. 2 (February 1, 2017): 347–59. http://dx.doi.org/10.1175/bams-d-15-00283.1.

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Abstract There are numerous challenges with the forecasting and detection of flash floods, one of the deadliest weather phenomena in the United States. Statistical metrics of flash flood warnings over recent years depict a generally stagnant warning performance, while regional flash flood guidance utilized in warning operations was shown to have low skill scores. The Hydrometeorological Testbed—Hydrology (HMT-Hydro) experiment was created to allow operational forecasters to assess emerging products and techniques designed to improve the prediction and warning of flash flooding. Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the Multi-Radar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations. The HMT-Hydro experiment ran in collaboration with the Flash Flood and Intense Rainfall (FFaIR) experiment at the Weather Prediction Center to simulate the real-time workflow between a national center and a local forecast office, as well as to facilitate discussions on the challenges of short-term flash flood forecasting. Results from the HMT-Hydro experiment highlighted the utility of MRMS and FLASH products in identifying the spatial coverage and magnitude of flash flooding, while evaluating the perception and reliability of probabilistic forecasts in flash flood watches and warnings. NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations.
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Dissertations / Theses on the topic "Flood and Flash Flood forecasting"

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Zevin, Susan Faye 1949. "A probabilistic approach to flash flood forecasting." Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/191119.

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A major area targeted for hydrometeorological forecast service improvements is in flash flood forecasting. Verification data show that general public service products of flash flood forecasts do not provide enough lead time in order for the public to make effective response. Sophisticated users of flash flood forecasts could use forecast probabilities of flash flooding in order to make decisions in preparation for the predicted event. To this end, a systematic probabilistic approach to flash flood forecasting is presented. The work first describes a deterministic system which serves as a conceptual basis for the probability system. The approach uses accumulated rainfall plus potential rainfall over a specified area and time period, and assesses this amount against the water holding capacity of the affected basin. These parameters are modeled as random variables in the probabilistic approach. The effects of uncertain measurements of rainfall and forecasts of precipitation from multiple information sources within a time period and moving forward in time are resolved through the use of Bayes' Theorem. The effect of uncertain inflows and outflows of atmospheric moisture on the states of the system, the transformation of variables, is resolved by use of convolution. Requirements for probability distributions to satisfy Bayes' Theorem are discussed in terms of the types and physical basis of meteorological data needed. The feasibility of obtaining the data is evaluated. Two alternatives for calculating the soil moisture deficit are presented--one, an online automatic rainfall/runoff model, the other an approximation. Using the soil moisture approximation, a software program was developed to test the probabilistic approach. A storm event was simulated and compared against an actual flash flood event. Results of the simulation improved forecast lead time by 3-5 hours over the actual forecasts issued at the time of the event.
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Keefer, Timothy Orrin, and Timothy Orrin Keefer. "Likelihood development for a probabilistic flash flood forecasting model." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/192077.

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An empirical method is developed for constructing likelihood functions required in a Bayesian probabilistic flash flood forecasting model using data on objective quantitative precipitation forecasts and their verification. Likelihoods based on categorical and probabilistic forecast information for several forecast periods, seasons, and locations are shown and compared. Data record length, forecast information type and magnitude, grid area, and discretized interval size are shown to affect probabilistic differentiation of amounts of potential rainfall. Use of these likelihoods in Bayes' Theorem to update prior probability distributions of potential rainfall, based on preliminary data, to posterior probability distributions, reflecting the latest forecast information, demonstrates that an abbreviated version of the flash flood forecasting methodology is currently practicable. For this application, likelihoods based on the categorical forecast are indicated. Apart from flash flood forecasting, it is shown that likelihoods can provide detailed insight into the value of information contained in particular forecast products.
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Brong, Brian S. "A study of flash flood potential in western Nevada and eastern California to enhance flash flood forecasting and awareness." abstract and full text PDF (free order & download UNR users only), 2005. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433282.

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Hatter, Elizabeth. "Using radar and hydrologic data to improve forecasts of flash floods in Missouri /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1422929.

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Yatheendradas, Soni. "Flash Flood Forecasting for the Semi-Arid Southwestern United States." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195244.

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Flash flooding in the semi-arid United States poses a significant danger to life and property. One effective way to mitigate flood risk is by implementing a rainfall-runoff model in a real-time forecast and warning system. This study investigated the feasibility of using the mechanistic, distributed semi-arid rainfall-runoff model KINEROS2 driven by high resolution radar rainfall input estimates obtained from the NEXRAD WSR-88D DHR reflectivity measurements in such a system. The original procedural paradigm-based KINEROS2 Fortran 77 code with space-time looping was recoded into an object-oriented Fortran 90 code with time-space looping for this purpose. The recoded form is now applicable to large basins, is easily future-extensible, and individual modules can be incorporated into other models.Sources of operational uncertainty in the above system were investigated for their influence over several events within a sub-basin of the USDA-ARS Walnut Gulch Experimental Watershed. Uncertainties considered were in the rainfall estimates, the model parameters, and the initial conditions. The variance-based Sobol' method of global sensitivity analysis conditioned on the observed streamflow showed that the uncertainty in the modeled response was heavily dominated by the operational variability of biases in the radar rainfall depth estimates. Sensitivities to KINEROS2 parameters indicates the need for improved representation of semi-arid hillslope hydrology in small basins, while pointing to specific influential, but poorly identified model parameters towards which field investigations should be directed. The significant influence of initial hillslope soil moisture showed the requirement of a sophisticated inter-storm model component for a continuous forecasting model.A synthetic study data was used to further explore the phenomena seen in the above real data study, of behavioral modifier set inconsistency across all events and of irreducibility in the spatial modifier ranges. The former was found to be attributable to wide uncertainty ranges in the sources of uncertainty, and the latter to the high distributed model non-linearity with associated interactions. These contribute towards a high predictive uncertainty in operational forecasting.Overall, the GLUE-based predictive uncertainty method with behavioral classification and accommodation of wide operational source uncertainty ranges is recommended as a simple and effective setup for operational flash flood forecasting.
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Terti, Galateia. "Forecasting of flash-flood human impacts integrating the social vulnerability dynamics." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAU004/document.

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Au XXIe siècle, la prévision de l'aléa hydrométéorologique et des impacts associés aux crues rapides demeurent un défi pour les prévisionnistes et les services de secours. Les mesures structurelles et / ou les avancées des systèmes de prévision hydrologique ne garantissent pas, à elles seules, la réduction des décès lors de ces phénomènes d'inondation rapide. La littérature souligne la nécessité d'intégrer d'autres facteurs, liés aux processus de vulnérabilité sociaux et comportementaux, afin de mieux prendre en compte les risques encourus par les populations lors de ces épisodes extrêmes. Cette dissertation conduit une analyse théorique couplés à ceux de une analyse des accidents historiques mortels afin d'expliquer les interactions qui existent entre les processus hydrométéorologiques et sociaux responsables de l'apparition de vulnérabilités humaines lors de crues rapides aux États-Unis. Des données d'enquêtes liées aux crues rapides sont examinées afin d'élaborer un système de classification des circonstances du décès (en voiture, à l'extérieur, à proximité d'un cours d'eau, dans un camping, dans un bâtiment ou en mobile-home). L'objectif est d'établir un lien entre la conception des vulnérabilités et l'estimation des pertes humaines liées à ces catastrophes naturelles. "Random forest" est utilisé et est basé sur un arbre de décision, qui permet d'évaluer la probabilité d'occurrence de décès pour une circonstance donnée en fonction d'indicateurs spatio-temporels. Un système de prévision des décès liés à l'usage de la voiture lors des crues rapides, circonstance la plus répandue, est donc proposé en s'appuyant sur les indicateurs initialement identifiés lors de l'étude théorique. Les résultats confirment que la vulnérabilité humaine et le risque associé varient de façon dynamique et infra journalière, et en fonction de la résonance spatio-temporelle entre la dynamique sociale et la dynamique d'exposition aux dangers. Par exemple, on constate que les jeunes et les personnes d'âge moyen sont plus susceptibles de se retrouver pris au piège des crues rapides particulièrement soudaines(par exemple, une durée de près de 5 heures) pendant les horaires de travail ou de loisirs en extérieur. Les personnes âgées sont quant à elles plus susceptibles de périr à l'intérieur des bâtiments, lors d'inondations plus longues, et surtout pendant la nuit lorsque les opérations de sauvetage et / ou d'évacuation sont rendues difficiles. Ces résultats mettent en évidence l'importance d'examiner la situation d'exposition aux risques en tenant compte de la vulnérabilité dynamique, plutôt que de se concentrer sur les conceptualisations génériques et statiques. Ce concept de vulnérabilité dynamique est l'objectif de modélisation développée dans cette thèse pour des vulnérabilités liés aux véhicules. À partir de l'étude de cas sur les crues rapides survenues en mai 2015, et en analysant principalement les états du Texas et de l'Oklahoma, principaux états infectés par ces évènements,le modèle montre des résultats prometteurs en termes d'identification spatio-temporelle des circonstances dangereuses. Cependant, des seuils critiques pour la prédiction des incidents liés aux véhicules doivent être étudiés plus en profondeur en intégrant des sensibilités locales non encore résolues par le modèle. Le modèle établi peut être appliqué, à une résolution journalière ou horaire, pour chaque comté du continent américain. Nous envisageons cette approche comme une première étape afin de fournir un système de prévision des crues rapides et des risques associés sur le continent américain. Il est important que la communauté scientifique spécialisée dans l'étude des crues éclairs récoltent des données à plus haute résolution lorsque ces épisodes entrainement des risques mortels, et ce afin d'appuyer la modélisation des complexités temporelles et spatiales associées aux pertes humaines causées par les futures inondations soudaines
In the 21st century the prediction of and subsequent response to impacts due to sudden onset and localized flash flooding events remain a challenge for forecasters and emergency managers. Structural measures and/or advances in hydrological forecasting systems alone do not guarantee reduction of fatalities during short-fuse flood events. The literature highlights the need for the integration of additional factors related to social and behavioral vulnerability processes to better capture risk of people during flash floods. This dissertation conducts a theoretical analysis as well as an analysis of flash flood-specific historic fatalities to explain complex and dynamic interactions between hydrometeorological, spatial and social processes responsible for the occurrence of human life-threatening situations during the "event" phase of flash floods in the United States (U.S.). Individual-by-individual fatality records are examined in order to develop a classification system of circumstances (i.e., vehicle-related, outside/close to streams, campsite, permanent buildings, and mobile homes). The ultimate goal is to link human vulnerability conceptualizations with realistic forecasts of prominent human losses from flash flood hazards. Random forest, a well-known decision-tree based ensemble machine learning algorithm for classification is adopted to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators at the county-level and daily or hourly time steps. Starting from the most prevalent circumstance of fatalities raised from both the literature review and the impact-based analysis, flash flood events with lethal vehicle-related accidents are the subject to predict. The findings confirm that human vulnerability and the subsequent risk to flash flooding, vary dynamically depending on the space-time resonance between that social and hazard dynamics. For example, it is found that younger and middle-aged people are more probable to get trapped from very fast flash floods (e.g., duration close to 5 hours) while participating in daytime outdoor activities (e.g., vehicle-related, recreational). In contrary, older people are more likely to perish from longer flooding inside buildings, and especially in twilight and darkness hours when rescue and/or evacuation operations are hindered. This reasoning places the importance of situational examination of dynamic vulnerability over generic and static conceptualizations, and guides the development of flash flood-specific modeling of vehicle-related human risk in this thesis. Based on the case study of May 2015 flash floods with a focus in Texas and Oklahoma, the model shows promising results in terms of identifying dangerous circumstances in space and time. Though, critical thresholds for the prediction of vehicle-related incidents need to be further investigated integrating local sensitivities, not yet captured by the model. The developed model can be applied on a daily or hourly basis for every U.S. county. We vision this approach as a first effort to provide a prediction system to support emergency preparedness and response to flash flood disasters over the conterminous U.S. It is recommended that the flash flood disaster science community and practitioners conduct data collection with more details for the life-threatening scene, and at finer resolutions to support modeling of local temporal and spatial complexities associated with human losses from flash flooding in the future
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Khajehei, Sepideh. "From Probabilistic Socio-Economic Vulnerability to an Integrated Framework for Flash Flood Prediction." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4666.

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Flash flood is among the most hazardous natural disasters, and it can cause severe damages to the environment and human life. Flash floods are mainly caused by intense rainfall and due to their rapid onset (within six hours of rainfall), very limited opportunity can be left for effective response. Understanding the socio-economic characteristics involving natural hazards potential, vulnerability, and resilience is necessary to address the damages to economy and casualties from extreme natural hazards. The vulnerability to flash floods is dependent on both biophysical and socio-economic factors. This study provides a comprehensive assessment of socio-economic vulnerability to flash flood alongside a novel framework for flash flood early warning system. A socio-economic vulnerability index was developed for each state and county in the Contiguous United States (CONUS). For this purpose, extensive ensembles of social and economic variables from US Census and the Bureau of Economic Analysis were assessed. The coincidence of socio-economic vulnerability and flash flood events were investigated to diagnose the critical and non-critical regions. In addition, a data-analytic approach is developed to assess the interaction between flash flood characteristics and the hydroclimatic variables, which is then applied as the foundation of the flash flood warning system. A novel framework based on the D-vine copula quantile regression algorithm is developed to detect the most significant hydroclimatic variables that describe the flash flood magnitude and duration as response variables and estimate the conditional quantiles of the flash flood characteristics. This study can help mitigate flash flood risks and improve recovery planning, and it can be useful for reducing flash flood impacts on vulnerable regions and population.
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Michaud, Jene Diane. "RAINFALL-RUNOFF MODELING OF FLASH FLOODS IN SEMI-ARID WATERSHEDS." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1992. http://hdl.handle.net/10150/614156.

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Flash floods caused by localized thunderstorms are a natural hazard of the semi -arid Southwest, and many communities have responded by installing ALERT flood forecasting systems. This study explored a rainfall- runoff modeling approach thought to be appropriate for forecasting in such watersheds. The kinematic model KINEROS was evaluated because it is a distributed model developed specifically for desert regions, and can be applied to basins without historic data. This study examined the accuracy of KINEROS under data constraints that are typical of semi -arid ALERT watersheds. The model was validated at the 150 km2, semi -arid Walnut Gulch experimental watershed. Under the conditions examined, KINEROS provided poor simulations of runoff volume and peak flow, but good simulations of time to peak. For peak flows, the standard error of estimate was nearly 100% of the observed mean. Surprisingly, when model parameters were based only on measurable watershed properties, simulated peak flows were as accurate as when parameters were calibrated on some historic data. The accuracy of KINEROS was compared to that of the SCS model. When calibrated, a distributed SCS model with a simple channel loss component was as accurate as KINEROS. Reasons for poor simulations were investigated by examining a) rainfall sampling errors, b) model sensitivity and dynamics, and c) trends in simulation accuracy. The cause of poor simulations was divided between rainfall sampling errors and other problems. It was found that when raingage densities are on the order of 1/20 km2, rainfall sampling errors preclude the consistent and reliable simulation of runoff from localized thunderstorms. Even when rainfall errors were minimized, accuracy of simulations were still poor. Good results, however, have been obtained with KINEROS on small watersheds; the problem is not KINEROS itself but its application at larger scales. The study also examined the hydrology of thunderstorm -generated floods at Walnut Gulch. The space -time dynamics of rainfall and runoff were characterized and found to be of fundamental importance. Hillslope infiltration was found to exert a dominant control on runoff, although flow hydraulics, channel losses, and initial soil moisture are also important. Watershed response was found to be nonlinear.
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Lincoln, W. Scott. "A modeling approach for operational flash flood forecasting for small-scale watersheds in central Iowa." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1468110.

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Norbiato, Daniele. "Regional analysis of flooding and flash flooding." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425502.

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Improving the capacity to make predictions in ungauged basins is one of most difficult challenge for the scientific community (see for example the current initiative Prediction Ungaged Basins (PUB) launched by the International Association of Hydrological Sciences, IAHS). Whatever hydrological models are used, in view of the tremendous spatio-temporal heterogeneity of climatic and landscape properties, extrapolation of information, or knowledge, from gauged to ungauged basins remains fraught with considerable difficulties and uncertainties, especially in the light of the generally poor understanding of where water goes when it rains, what flow path it takes to the stream, and the age of the water that emerges in the channel. The PUB problem is the key concept of this thesis and it is analysed from several point of view. Methodologies able to observe, model and predict the hydrological response at the regional scale are proposed.
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Books on the topic "Flood and Flash Flood forecasting"

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United States. Office of Hydrology, ed. Modernized areal flash flood guidance. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, Office of Hydrology, 1992.

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United States. National Weather Service, ed. Flash floods and floods--: The awesome power! : a preparedness guide. [Washington, D.C.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1992.

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Fleming, Eric L. Characteristics of western region flash flood events in GOES imagery and conventional data. Rockville, MD: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1986.

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Campbell, A. Kristine. 1982 and 1983 watch/warning verification: Flash flood, winter storm and high wind. Silver Spring, MD: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1985.

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C, Newton Mary, and United States. National Weather Service. Techniques Development Laboratory., eds. AFOS monitoring of MDR data using flash flood guidance. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1987.

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(Korea), Kungnip Pangjae Yŏn'guso. Tolbal hongsu yech'ŭk sisŭt'em hwakchang mit ŭisa kyŏlchŏng chiwŏn sisŭt'em p'ŭrosesŭ kaebal: Developing the flash flood prediction & decision-making support system in mountainous area. Sŏul T'ŭkpyŏlsi: Kungnip Pangjae Kyoyuk Yŏn'guwŏn Pangjae Yŏn'guso, 2009.

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Scofield, Roderick A. The use of water vapor for detecting environments that lead to convectively produced heavy precipitation and flash floods. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 2000.

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Scofield, Roderick A. The use of water vapor for detecting environments that lead to convectively produced heavy precipitation and flash floods. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 2000.

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Scofield, Roderick A. The use of water vapor for detecting environments that lead to convectively produced heavy precipitation and flash floods. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 2000.

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Scofield, Roderick A. The use of water vapor for detecting environments that lead to convectively produced heavy precipitation and flash floods. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 2000.

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Book chapters on the topic "Flood and Flash Flood forecasting"

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Sene, Kevin. "Flood Forecasting." In Flash Floods, 133–68. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5164-4_5.

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Boutaghane, Hamouda, Tayeb Boulmaiz, El Khansa Lameche, Abdelouahab Lefkir, Mahmoud Hasbaia, Chérifa Abdelbaki, Ahmed Walid Moulahoum, Mehdi Keblouti, and Abdelmalek Bermad. "Flood Analysis and Mitigation Strategies in Algeria." In Natural Disaster Science and Mitigation Engineering: DPRI reports, 95–118. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2904-4_3.

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AbstractFloods are frequent hazard in Algeria. They cause severe casualties, destroy infrastructures, and impair economies. In the past decades, Algeria experienced devastating floods. The dominant type of occurring floods are flash floods, which tend to be not well documented and studied in Algeria. This chapter presents a brief introduction to the flood phenomena within the Algerian climatic and management context, based on databases, scientific publications, and local technical reports. Existing studies about floods are reviewed. It also provides an analysis of the most disastrous floods that occurred in the past decades. Of the most noteworthy flash floods, a highlight of the Bab El Oued flash flood occurring in a heavily urbanized setting and the M’zab Valley flash flood, which took place in a UNESCO World Heritage Site. The monitoring network in Algeria is presented and data availability is discussed. The implementation of the first forecasting and early warning system are also presented. Different aspects of flash floods were presented including the effect of the increase of urbanization, the influence of climate change and the adopted strategies of flood risk management. Heavy and increasing urbanization and population growth increased the flood vulnerability and this trend must be mitigated.
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Alfieri, Lorenzo, Marc Berenguer, Valentin Knechtl, Katharina Liechti, Daniel Sempere-Torres, and Massimiliano Zappa. "Flash Flood Forecasting Based on Rainfall Thresholds." In Handbook of Hydrometeorological Ensemble Forecasting, 1–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40457-3_49-1.

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Alfieri, Lorenzo, Marc Berenguer, Valentin Knechtl, Katharina Liechti, Daniel Sempere-Torres, and Massimiliano Zappa. "Flash Flood Forecasting Based on Rainfall Thresholds." In Handbook of Hydrometeorological Ensemble Forecasting, 1223–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-39925-1_49.

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Brahm Parkash Yadav, A., B. S. K. Asok Raja, C. Rahul Saxena, D. Hemlata Bharwani, E. Ashok Kumar Das, F. Ram Kumar Giri, G. S. K. Manik, and H. Deepak Yadav. "Recent Advances in Pluvial Flash Flood Forecasting of India." In Lecture Notes in Civil Engineering, 605–43. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0304-5_44.

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Březková, Lucie, Milan Šálek, Petr Novák, Hana Kyznarová, and Martin Jonov. "New Methods of Flash Flood Forecasting in the Czech Republic." In IFIP Advances in Information and Communication Technology, 550–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22285-6_59.

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Mooktaree, Apimook, Piyamarn Sisomphon, Sathit Chantip, and Ticha Lolupiman. "Improving the Efficiency of Flash Flood Forecasting and Warning System in Thailand." In Advances in Hydroinformatics, 437–53. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1600-7_28.

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Wang, Wen-Chuan, Yan-Wei Zhao, Chang-Jun Liu, Qiang Ma, and Dong-Mei Xu. "Study on Forecasting and Alarming Model of Flash Flood Based on Machine Learning." In Advances in Hydroinformatics, 455–69. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1600-7_29.

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Khan, Nusrat, and Md Mostafa Ali. "Flash Flood Forecasting of Jadukata River Basin at Laurergarh, Sunamganj from Real Time Satellite Precipitation Product by Using HEC-HMS." In Water, Flood Management and Water Security Under a Changing Climate, 201–8. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47786-8_14.

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Toukourou, Mohamed Samir, Anne Johannet, and Gérard Dreyfus. "Flash Flood Forecasting by Statistical Learning in the Absence of Rainfall Forecast: A Case Study." In Engineering Applications of Neural Networks, 98–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03969-0_10.

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Conference papers on the topic "Flood and Flash Flood forecasting"

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Yordanova, Valeriya, Silviya Stoyanova, Snezhanka Balabanova, Georgy Koshinchanov, and Vesela Stoyanova. "FLASH FLOOD FORECASTING USING FLASH FLOOD GUIDANCE SYSTEM PRODUCTS." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/3.1/s12.11.

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Flash floods are defined as rapidly developing extreme events caused by heavy or excessive amounts of rainfall. Flash floods usually occur over a relatively small area within six hours or less of the extreme event with quite a rapid streamflow rise and fall. Increased occurrence of flash flood events is expected due to climate change and increase in extreme precipitation events [1]. Flash flood forecasting is still a challenge for hydrologists and water professionals due to the complex nature of the event itself. Besides having sufficient background in hydrological and meteorological forecasting as well as information about local conditions yet an adequate approach for flash flood forecasting is needed. The Flash Flood Guidance System (FFGS) is widely recognized for enhancing the capacity to issue timely and accurate flash flood warnings by providing hydrological and meteorological forecasters with real-time information and products. FFGS is based on global data as well as national hydrometeorological data and analyses. In this paper the use of the Black Sea Middle East Flash Flood Guidance System (BSMEFFGS) products for flash flood forecasting by the hydrologists at the Hydrological Forecasting department at the National Institute of Meteorology and Hydrology, Bulgarian Academy of Sciences (NIMH) in Bulgaria is presented. An overview of the FFGS for Bulgaria with closer attention paid to the Flash Flood Guidance (FFG), Flash Flood Risk (FFR) and the Flash Flood Threat Products is introduced. Two case studies are also presented � a flash flood in the town of Shumen and another one in the area of the village of Popovitsa on September 28th 2015.
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Janál, Petr, and Tomáš Kozel. "FUZZY LOGIC BASED FLASH FLOOD FORECAST." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.10.

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The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurrence was assembled on the bases of the fuzzy logic. The model covers whole area of the Czech Republic and the nearest surroundings. The domain is divided into 3245 small catchments of the average size of 30 km2. Real flood episodes were used for the calibration and future flood events can be used for recalibration (principle of adaptability). The model consists of two fuzzy inference systems (FIS). The catchment predisposition for the flash flood occurrence is evaluated by the first FIS. The geomorphological characteristics and long-term meteorological statistics serve as the inputs. The second FIS evaluates real-time data. The inputs are: The predisposition for flash flood occurrence (gained from the first FIS), the rainfall intensity, the rainfall duration and the antecedent precipitation index. The meteorological radar measurement and the precipitation nowcasting serve as the precipitation data source. Various precipitation nowcasting methods are considered. The risk of the flash flood occurrence is evaluated for each small catchment every 5 or 10 minutes (the time step depends on the precipitation nowcasting method). The Fuzzy Flash Flood model is implemented in the Czech Hydrometeorological Institute (CHMI) – Brno Regional Office. The results are available for all forecasters at CHMI via web application for testing. The huge uncertainty inherent in the flash flood forecasting causes that fuzzy model outputs based on different nowcasting methods could vary significantly. The storms development is very dynamic and hydrological forecast could change a lot of every 5 minutes. That is why the fuzzy model estimates are intended to be used by experts only. The Fuzzy Flash Flood model is an alternative tool for the flash flood forecasting. It can provide the first hints of danger of flash flood occurrence within the whole territory of the Czech Republic. Its main advantage is very fast calculation and possibility of variant approach using various precipitation nowcasting inputs. However, the system produces large number of false alarms, therefore the long-term testing in operation is necessary and the warning releasing rules must be set.
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Ziyue Zeng, Gouqiang Tang, Di Long, Hui Xu, Yun Chen, and Yang Hong. "Development of GIS-based FFPI for China's flash flood forecasting." In 2015 23rd International Conference on Geoinformatics. IEEE, 2015. http://dx.doi.org/10.1109/geoinformatics.2015.7378697.

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Khan, Talha, Kushairy Kadir, Muhammad Alcm, Zeeshan Fchiihid, and M. S. Mazliham. "Geomagnetic field measurement at earth surface: Flash flood forecasting using tesla meter." In 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T). IEEE, 2017. http://dx.doi.org/10.1109/ice2t.2017.8215991.

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Boukharouba, Khaled, Pierre Roussel, Gerard Dreyfus, and Anne Johannet. "Flash flood forecasting using Support Vector Regression: An event clustering based approach." In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2013. http://dx.doi.org/10.1109/mlsp.2013.6661958.

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Sagar Sudhakar Mane and M. K. Mokashi. "Real-Time Flash-Flood Monitoring, Alerting and Forecasting System using Data Mining and wireless sensor Network." In 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2015. http://dx.doi.org/10.1109/iccsp.2015.7322851.

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Cosgrove, Brian A., Seann Reed, Feng Ding, Yu Zhang, Zhengtao Cui, and Ziya Zhang. "Flash Flood Forecasting for Ungauged Locations with NEXRAD Precipitation Data, Threshold Frequencies, and a Distributed Hydrologic Model." In World Environmental and Water Resources Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41036(342)622.

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"A coupled hydrological-hydraulic flash flood forecasting system for Kuala Lumpur's Stormwater Management and Road Tunnel (SMART)." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.l19.cohen.

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Rahman, Md Mafizur, Md Sabbir Mostafa Khan, and Md Fayzul Kabir Pasha. "Simple Approach for Flood Forecasting." In Joint Conference on Water Resource Engineering and Water Resources Planning and Management 2000. Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40517(2000)413.

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Abbassi, Kamel, Hamadi Lirathni, Mohamed Hechmi Jeridi, and Tahar Ezzedine. "Flood Forecasting with Bayesian Approach." In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2021. http://dx.doi.org/10.23919/softcom52868.2021.9559122.

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Reports on the topic "Flood and Flash Flood forecasting"

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Shrestha, A. B., and S. R. Bajracharya. Case Studies on Flash Flood Risk Management in the Himalayas; In support of specific flash flood policies. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2013. http://dx.doi.org/10.53055/icimod.577.

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Peters, John C. Application of Rainfall-Runoff Simulation for Flood Forecasting. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada273140.

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Shrestha, A. B., P. S. Chapagain, and R. Thapa. Flash Flood Risk Management; A Training of Trainers Manual. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2011. http://dx.doi.org/10.53055/icimod.541.

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Shrestha, A. B., P. S. Chapagain, and R. Thapa. Flash Flood Risk Management; A Training of Trainers Manual. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2011. http://dx.doi.org/10.53055/icimod.541.

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Teillet, P. M., R. P. Gauthier, T. J. Pultz, A. Deschamps, G. Fedosejevs, M. Maloley, G. Ainsley, and A. Chichagov. A Soil Moisture Sensorweb for Use in Flood Forecasting Applications. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220059.

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Shrestha, A. B., G. C. Ezee, R. P. Adhikary, and S. K. Rai. Resource Manual on Flash Flood Risk Management; Module 3 - Structural Measures. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2012. http://dx.doi.org/10.53055/icimod.570.

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Shrestha, A. B., G. C. Ezee, R. P. Adhikary, and S. K. Rai. Resource Manual on Flash Flood Risk Management; Module 3 - Structural Measures. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2012. http://dx.doi.org/10.53055/icimod.570.

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Shrestha, A. B., R. Karim, and S. H. Shah. Resource Manual on Flash Flood Risk Management; Module 1: Community-based Management. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.490.

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Shrestha, A. B. Resource Manual on Flash Flood Risk Management; Module 2: Non-Structural Measures. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.491.

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Shrestha, A. B., R. Karim, and S. H. Shah. Resource Manual on Flash Flood Risk Management; Module 1: Community-based Management. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.490.

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